The dynamic mode decomposition (DMD) is a data diagnostic technique that
extracts coherent spatial-temporal patterns from high-dimensional time series data [1]}, [2]}.
Although DMD originated in the fluid dynamics community [1]},
the algorithm has since been applied to a wealth of dynamical systems including in
epidemiology [4]},
robotics [5]}, [6]},
neuroscience [7]},
quantum control [8]},
power grids [9]},
and plasma physics [10]}, [11]}.
Despite its widespread successes, DMD is highly sensitive to noise [12]}, [13]}, [14]},
fails to capture travelling wave physics,
and can produce overfit models that do not generalize.
Herein, we demonstrate that integrating physics into the learning framework can help address these challenges.
